基于私有跨筒仓联邦学习的疫苗不良事件提及信息提取
Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions
摘要：
联邦学习（FL）正在迅速成为一个goto分布式训练范例，用户联合训练一个全球模型，而无需物理共享他们的数据。用户可以间接地对用于训练全局模型的大得多的聚合数据语料库做出贡献，并且直接受益于该数据语料库。然而，在现实世界中的问题设置FL的成功应用的文献是有点稀疏。在本文中，我们描述了我们的经验，应用FL为基础的解决方案的命名实体识别（NER）任务的大规模疫苗接种计划的背景下的不良事件检测应用。我们提出了一个全面的实证分析的各个方面的好处与外语为基础的培训。此外，我们调查的影响，更严格的差分隐私（DP）的限制，在高度敏感的设置，联合用户必须执行本地DP，以确保严格的隐私保证。我们发现，本地DP可以严重削弱全球模型的预测精度，从而抑制用户参与联邦。作为回应，我们展示了个性化方法的最新创新如何帮助显着恢复丢失的准确性。我们把我们的分析集中在联邦微调算法，FedFT，并证明它不是PAC可识别的，从而使它更有吸引力的FL为基础的培训。
英文摘要：
Federated Learning (FL) is quickly becoming a goto distributed training paradigm for users to jointly train a global model without physically sharing their data. Users can indirectly contribute to, and directly benefit from a much larger aggregate data corpus used to train the global model. However, literature on successful application of FL in real-world problem settings is somewhat sparse. In this paper, we describe our experience applying a FL based solution to the Named Entity Recognition (NER) task for an adverse event detection application in the context of mass scale vaccination programs. We present a comprehensive empirical analysis of various dimensions of benefits gained with FL based training. Furthermore, we investigate effects of tighter Differential Privacy (DP) constraints in highly sensitive settings where federation users must enforce Local DP to ensure strict privacy guarantees. We show that local DP can severely cripple the global model's prediction accuracy, thus dis-incentivizing users from participating in the federation. In response, we demonstrate how recent innovation on personalization methods can help significantly recover the lost accuracy. We focus our analysis on the Federated Fine-Tuning algorithm, FedFT, and prove that it is not PAC Identifiable, thus making it even more attractive for FL-based training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
